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问题:

I'm trying to learn how to make GPU optimalized OpenCL kernells, I took example of matrix multiplication using square tiles in local memory. However I got at best case just ~10-times speedup ( ~50 Gflops ) in comparison to numpy.dot() ( 5 Gflops , it is using BLAS).

I found studies where they got speedup >200x ( >1000 Gflops ). ftp://ftp.u-aizu.ac.jp/u-aizu/doc/Tech-Report/2012/2012-002.pdf I don't know what I'm doing wrong, or if it is just because of my GPU ( nvidia GTX 275 ). Or if it is because of some pyOpenCl overhead. But I meassured also how long does take just to copy result from GPU to RAM and it is just ~10% of the matrix multiplication time.

#define BLOCK_SIZE 22 
__kernel void matrixMul(
      __global float* Cij, 
      __global float* Aik, 
      __global float* Bkj, 
      __const int ni, 
      __const int nj,
      __const int nk
){
//   WARRNING : interchange of  i  and  j  dimension  lower the performance >2x on my nV GT275 GPU    
int gj = get_global_id(0);    int gi = get_global_id(1); 
int bj = get_group_id(0);     int bi = get_group_id(1);  // Block index
int tj = get_local_id(0);     int ti = get_local_id(1);  // Thread index
int oj = bi*BLOCK_SIZE;       int oi = bj*BLOCK_SIZE; 
float Csub =0; 
__local float As   [BLOCK_SIZE][BLOCK_SIZE];
__local float Bs   [BLOCK_SIZE][BLOCK_SIZE];
for (int ok = 0; ok < nk; ok += BLOCK_SIZE )   {
    As[ti][tj] = Aik[ nk*(gi   ) + tj + ok ];   // A[i][k]
    Bs[ti][tj] = Bkj[ nj*(ti+ok) + gj ];        // B[k][j]
    barrier(CLK_LOCAL_MEM_FENCE);
    for (int k = 0; k < BLOCK_SIZE; ++k) Csub += As[ti][k] * Bs[k][tj];
    barrier(CLK_LOCAL_MEM_FENCE);
}
Cij[ nj * ( gi ) + gj ] = Csub;

}

NOTE - the strange BLOCK_SIZE=22 is the maximum BLOCK_SIZE which does fit to max work_group_size which is 512 on my GPU. In this code must hold condition BLOCK_SIZE^2 < max work_group_size. 22=int(sqrt(512)). I tried also BLOCK_SIZE=16 or 8 but it was slower tan with 22.

I also tried simple matrixMul (without using local memory) but it was even 10-times slower than numpy.dot(). I copied the code here http://gpgpu-computing4.blogspot.cz/2009/10/matrix-multiplication-3-opencl.html they say that even the simple version (without local memory) should run 200x faster than CPU? I don't undrestand that.

the dependecne of performance in my case is:

N =  220 numpy 3.680 [Gflops] GPU 16.428 [Gflops] speedUp 4.464 
N =  330 numpy 4.752 [Gflops] GPU 29.487 [Gflops] speedUp 6.205 
N =  440 numpy 4.914 [Gflops] GPU 37.096 [Gflops] speedUp 7.548 
N =  550 numpy 3.849 [Gflops] GPU 47.019 [Gflops] speedUp 12.217 
N =  660 numpy 5.251 [Gflops] GPU 49.999 [Gflops] speedUp 9.522 
N =  770 numpy 4.565 [Gflops] GPU 48.567 [Gflops] speedUp 10.638 
N =  880 numpy 5.452 [Gflops] GPU 44.444 [Gflops] speedUp 8.152 
N =  990 numpy 4.976 [Gflops] GPU 42.187 [Gflops] speedUp 8.478 
N = 1100 numpy 5.324 [Gflops] GPU 83.187 [Gflops] speedUp 15.625 
N = 1210 numpy 5.401 [Gflops] GPU 57.147 [Gflops] speedUp 10.581 
N = 1320 numpy 5.450 [Gflops] GPU 48.936 [Gflops] speedUp 8.979

NOTE - the "Gflops" number is obtained as N^3/time and it does include time required to copy results from GPU to main memory, but this time is just few percent of total time especially for N>1000

maybe more pictorial is time in secons:

N =  220 numpy 0.003 [s] GPU 0.001 [s] load 0.001 [s] speedUp 5.000 
N =  330 numpy 0.008 [s] GPU 0.001 [s] load 0.001 [s] speedUp 7.683 
N =  440 numpy 0.017 [s] GPU 0.002 [s] load 0.001 [s] speedUp 7.565 
N =  550 numpy 0.043 [s] GPU 0.004 [s] load 0.001 [s] speedUp 11.957 
N =  660 numpy 0.055 [s] GPU 0.006 [s] load 0.002 [s] speedUp 9.298 
N =  770 numpy 0.100 [s] GPU 0.009 [s] load 0.003 [s] speedUp 10.638 
N =  880 numpy 0.125 [s] GPU 0.010 [s] load 0.000 [s] speedUp 12.097 
N =  990 numpy 0.195 [s] GPU 0.015 [s] load 0.000 [s] speedUp 12.581 
N = 1100 numpy 0.250 [s] GPU 0.031 [s] load 0.000 [s] speedUp 8.065 
N = 1210 numpy 0.328 [s] GPU 0.031 [s] load 0.000 [s] speedUp 10.581 
N = 1320 numpy 0.422 [s] GPU 0.047 [s] load 0.000 [s] speedUp 8.979

I was thinking that maybe some speed improvement can be obtained using async_work_group_copy or even read_imageui to copy blocks to local memory. But I don't understand why I have so big difference when I'm using basically the same code as people who say they have 200x speedup?????

回答1:

Without even looking at your code let me make some comments about your benchmarks. Let's ignore numpy and compare the maximum SP FLOPs/s and DP FLOPs/s of an Intel CPU versus Nvidia and AMD GPUs.

A Intel 2600K at 4 GHz can do 4 GHz * (8 AVX) * (2 ILP) * ( 4 cores) = 256 SP GFLOPs/s. For DP it's half: 128 DP GFLOPs/s. Haswell which comes out in a few weeks will double both of those. The Intel MKL library gets better than 80% efficiency in GEMM. My own GEMM code gets 70% on my i7-2700 so the 5 GFlops/s you quote with numpy is tiny and not fair to compare with.

I don't know what the GTX 275 is capable of but I would guess it's much more than 50 GFLOPs/s.

The article you reference compares a AMD 7970. They get 848 (90% efficiency) DP GFlops/s and 2646 (70% efficiency) SP GFlops/s. That's closer to 10x the performance of the CPU not 200x!

Edit: Your calculations of FLOPs is wrong it should be 2.0*n^3. That's still approximate but it's asymptotically true. Let me explain.

Consider a 3D dot product. It's x1*x2+y1*y2+z1*z2. That's 3 multiplications and two additions. So a N-dimensional dot product is n multiplications and (n-1) additions. A matrix product is equivalent to nxn dot products, i.e. n*n*n multiplications and n*n*(n-1) additions. That's approximately 2.0*n^3 FLOPS. So you should double all your Gflops/s numbers.

Edit: You might want to consider the kernel time. It's been awhile since I used OpenCL but using the C++ bindings I did something like this

queue = cl::CommandQueue(context, devices[device], CL_QUEUE_PROFILING_ENABLE|CL_QUEUE_OUT_OF_ORDER_EXEC_MODE_ENABLE, &err);
//other code...run kernel

time_end = clevent.getProfilingInfo<CL_PROFILING_COMMAND_END>();  
time_start = clevent.getProfilingInfo<CL_PROFILING_COMMAND_START>();

回答2:

A good GPU matrix-multiply does not just use local memory, it stores blocks of A, B, and/or C in registers (which results in higher register usage and lower occupancy but is much faster in the end). This is because GPUs have more registers than local memory (128-256KB vs 48KB for NVIDIA), and registers offer as much bandwidth as the ALUs can handle.